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import tensorflow as tf |
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import numpy as np |
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from tensorflow import keras |
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import os |
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from typing import Dict, List, Any |
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import pickle |
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from PIL import Image |
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class PreTrainedPipeline(): |
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def __init__(self, path=""): |
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self.decoder = keras.models.load_model(os.path.join(path, "decoder")) |
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self.decoder = keras.models.load_model(os.path.join(path, "encoder")) |
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image_model = tf.keras.applications.InceptionV3(include_top=False, |
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weights='imagenet') |
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new_input = image_model.input |
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hidden_layer = image_model.layers[-1].output |
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self.image_features_extract_model = tf.keras.Model(new_input, hidden_layer) |
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with open('tokenizer.pickle', 'rb') as handle: |
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self.tokenizer = pickle.load(handle) |
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def load_image(image_path): |
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img = tf.io.read_file(image_path) |
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img = tf.io.decode_jpeg(img, channels=3) |
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img = tf.image.resize(img, (299, 299)) |
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img = tf.keras.applications.inception_v3.preprocess_input(img) |
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return img, image_path |
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def __call__(self, inputs: "Image.Image") -> List[Dict[str, Any]]: |
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""" |
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Args: |
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inputs (:obj:`PIL.Image`): |
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The raw image representation as PIL. |
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No transformation made whatsoever from the input. Make all necessary transformations here. |
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Return: |
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A :obj:`list`:. The list contains items that are dicts should be liked {"label": "XXX", "score": 0.82} |
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It is preferred if the returned list is in decreasing `score` order |
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""" |
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hidden = tf.zeros((1, 512)) |
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temp_input = tf.expand_dims(load_image(image)[0], 0) |
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img_tensor_val = self.image_features_extract_model(temp_input) |
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img_tensor_val = tf.reshape(img_tensor_val, (img_tensor_val.shape[0], |
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-1, |
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img_tensor_val.shape[3])) |
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features = self.encoder(img_tensor_val) |
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dec_input = tf.expand_dims([self.tokenizer.word_index['<start>']], 0) |
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result = [] |
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for i in range(max_length): |
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predictions, hidden, attention_weights = self.decoder(dec_input, |
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features, |
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hidden) |
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predicted_id = tf.random.categorical(predictions, 1)[0][0].numpy() |
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result.append(self.tokenizer.index_word[predicted_id]) |
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if self.tokenizer.index_word[predicted_id] == '<end>': |
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return result |
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dec_input = tf.expand_dims([predicted_id], 0) |
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return result |
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